5 research outputs found
Collaborative Intelligent Cross-Camera Video Analytics at Edge: Opportunities and Challenges
Nowadays, video cameras are deployed in large scale for spatial monitoring of
physical places (e.g., surveillance systems in the context of smart cities).
The massive camera deployment, however, presents new challenges for analyzing
the enormous data, as the cost of high computational overhead of sophisticated
deep learning techniques imposes a prohibitive overhead, in terms of energy
consumption and processing throughput, on such resource-constrained edge
devices. To address these limitations, this paper envisions a collaborative
intelligent cross-camera video analytics paradigm at the network edge in which
camera nodes adjust their pipelines (e.g., inference) to incorporate correlated
observations and shared knowledge from other nodes' contents. By harassing
redundant spatio-temporal to reduce the size of the inference search space in
one hand, and intelligent collaboration between video nodes on the other, we
discuss how such collaborative paradigm can considerably improve accuracy,
reduce latency and decrease communication bandwidth compared to
non-collaborative baselines. This paper also describes major opportunities and
challenges in realizing such a paradigm.Comment: First International Workshop on Challenges in Artificial Intelligence
and Machine Learnin